With the widespread usage of social media platforms, such as Facebook™ LinkedIn™, Twitter™, and/or Instagram™, millions of registered users are able to interact with each other and express their emotions about various events, such as personal events, professional events, or other events occurring around them. Such interaction of the users on the social media platforms generates a vast amount of data that may offer various key insights about preferences and interests of the users. However, such data from the social media platforms may be noisy and unstructured. Additionally, the data is multi-modal in form of text, images, videos, and/or the like, which may make mining useful information to ascertain user's interest a hard problem. Further, various efforts have been made for mining the useful information about the user's interests from profiles of the users on the social media platforms. While these profiles may contain some explicit information about the user's interests, more often than not, user's interests are implicit in user's actions on the social media platforms. Therefore, there is a need for a method and a system that can facilitate the processing of the data extracted from the social media platforms and hence, inferring the useful information about the user's interests in much easier, efficient, useful, and displayable manner.
Further, limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.